System Implementing Machine Learning in Complex Multivariate Wafer Processing Equipment

ABSTRACT

A system for controlling processing state of a plasma process is provided. One example system includes a plasma reactor having a plurality of tuning knobs for making settings to operational conditions of the plasma reactor. A plurality of sensors of the plasma reactor is included, where each of the plurality of sensors is configured to produce a data stream of information during operation of the plasma reactor for carrying out the plasma process. A controller of the plasma reactor is configured to execute a multivariate processing that is configured to use as input desired processing state values that define intended measurable conditions within a processing environment of the plasma reactor and identify current plasma processing values. The multivariate processing uses a machine learning engine that receives as inputs the desired processing state values and data streams from the plurality of sensors during processing of the plasma process. The machine learning engine is configured to identify current processing state values used to produce a compensation vector, such that the compensation vector defines differences between the desired process state values and the current processing state values. The controller is further configured to execute a compensation processing operation that transforms the compensation vector expressed in terms of measured conditions within the processing environment to changes of specific one or more of the tuning knobs of the plasma reactor.

CLAIM OF PRIORITY

This application is a divisional of and claims priority to U.S.application Ser. No. 15/268,472 filed on Sep. 16, 2016, entitled “Methodand Process of Implementing Machine Learning in Complex MultivariateWafer Processing Equipment,” which is incorporated herein by referencein its entirety.

FIELD OF THE INVENTION

The present embodiments relate to methods and computer implementedprocesses for characterizing processing states that are desired duringprocessing in a plasma reactor and using data streams collected duringplasma processing to make adjustments to processing parameters so that acurrent processing state best matches a desired processing state. Insome implementations, the adjustments made are adjustments to physicalsettings, e.g., knobs that control parameter settings of the plasmareactor, and such settings are configured to shift the processing towarda known desired processing state. Furthermore, in the various disclosedembodiments, disclosure is provided regarding ways of optimizing theprocessing of data streams collected from sensors of the plasma reactor,and processing the data streams to make adjustments based on machinelearning algorithms.

BACKGROUND

Plasma has long been employed to process substrates (e.g., wafers orflat panels) to form electronic products (e.g., integrated circuits orflat panel displays). Semiconductor wafers are typically placed in anetch chamber with a mask layer to direct the etch of underlyingmaterials. The etching process removes the underlying materials notcovered by the mask. Due to the volatile plasma conditions generatedinside of a chamber, the etch process may also remove material fromsurfaces of parts within the plasma chamber. Over time, the parts insidethe processing chamber will therefore wear and will accumulateparticulate matter and/or etch residues, which may alter the etchperformance and/or cause process drift. For this reason, in addition tothe need to replace consumable parts, there is also a need toperiodically perform wet clean operations (i.e., of the inside surfacesand/or parts of a chamber).

After a wet clean, the chamber must be reconditioned through varioussteps/processes (i.e., processed for wet clean recovery) before thechamber is allowed to proceed with production wafer processing. Thisprocess is sometimes referred to as chamber “seasoning.” Seasoningattempts to produce surface conditions that mimic a steady state. Whensteady state is achieved, the solution tends to be brittle, i.e. it isnot always universal from process to process or chamber to chamber.Perhaps even worse, the seasoning itself can be a significant fractionof the total tool utilization, e.g. seasoning for 15-70 hours while theprocess only runs for 150-250 hours. Obviously, this is a productivityhit, not to mention wasted energy consumption, cost of seasoning wafers,and high cost of consumables as they wear just to season them.Furthermore, while a chamber recovers from a wet clean, fabrication ofproduction wafers is stopped.

As is well known, etch and deposition reactors are very complexequipment with multiple variables to control correct wafer processingcondition. In modern semiconductor processing, the system has grown socomplex that it is non-trivial to explain different physical/chemicalprocesses using set of trivial physical equations. Due to thiscomplexity, it is difficult to model modern day etch/depositionprocesses in its full form with all gas/pressure/power/frequency inputs.This difficulty in modeling (and by extension predicting) has reducedplasma processing into an art which largely depends on the artist'sexperience level (in this case the engineer) and environment rather thana predictable science.

It is in this context that embodiments arise.

SUMMARY

Methods and systems for controlling processing state of a plasma reactorto initiate processing of production substrates and/or to determine aready state of a reactor after the reactor has been cleaned and needs tobe seasoned for subsequent production wafer processing, are disclosed.The method initiates processing of a substrate in the plasma reactorusing settings for tuning knobs of the plasma reactor that areapproximated to achieve desired processing state values. A plurality ofdata streams are received from the plasma reactor during the processingof the substrate. The plurality of data streams are used to identifycurrent processing state values. The method includes generating acompensation vector that identifies differences between the currentprocessing state values and the desired processing state values. Thegeneration of the compensation vector uses machine learning to improveand refile the identification and amount of compensation needed, asidentified in the compensation vector. The method further includestransforming the compensation vector into adjustments to the settingsfor the tuning knobs and then applying the adjustment to the tuningknobs of the plasma reactor.

In another embodiment, a system for controlling processing state of aplasma process is disclosed. The system includes a plasma reactor havinga plurality of tuning knobs for making settings to operationalconditions of the plasma reactor. A plurality of sensors of the plasmareactor is included, where each of the plurality of sensors isconfigured to produce a data stream of information during operation ofthe plasma reactor for carrying out the plasma process. A controller ofthe plasma reactor is configured to execute a multivariate processingthat is configured to use as input desired processing state values thatdefine intended measurable conditions within a processing environment ofthe plasma reactor and identify current plasma processing values. Themultivariate processing uses a machine learning engine that receives asinputs the desired processing state values and data streams from theplurality of sensors during processing of the plasma process. Themachine learning engine is configured to identify current processingstate values used to produce a compensation vector, such that thecompensation vector defines differences between the desired processstate values and the current processing state values. The controller isfurther configured to execute a compensation processing operation thattransforms the compensation vector expressed in terms of measuredconditions within the processing environment to changes of specific oneor more of the tuning knobs of the plasma reactor.

In some embodiments, a method further includes continuing to receive theplurality of data streams from the plasma reactor during the processingof the substrate to produce the adjustments to the settings of thetuning knobs to assist in moving the current processing state valuestoward the desired processing state values.

In some embodiments, the desired processing state and the currentprocessing state are defined in a virtual space that is descriptive of aphysical state of plasma conditions sensed within a processing volume ofthe plasma reactor by the sensors. By way of example, and withoutlimitation to others, the plasma conditions can be a set of detectedion, electron and neutral fluxes at a plane of the substrate, for aspecific reactor wall surface condition.

In some embodiments, the compensation vector identifies the differencesbetween the desired processing state values and the desired processingstate values in the virtual space. And, the transformation of thecompensation vector identifies the adjustments to the settings for theknobs as a set of identified physical knobs having an identifiedphysical adjustment. In one embodiment, a controller of the plasmareactor is configured to process program instructions that cause theadjustments of the settings to the knobs.

In some embodiments, a multivariate process is configured to identifythe differences between the current processing state values and thedesired processing state values. The multivariate process includesprocessing machine learning to make adjustments to the desiredprocessing state values to produce adjusted desired processing statevalues based, at least in part, on verification feedback received fromone or both of etch rate performance or monitor wafer performance of theprocessing of the substrate.

In some embodiments, the processing of the substrate is identified for aspecific plasma reactor and a specific process recipe, and each specificprocess recipe and each specific plasma reactor has an associated modelthat includes settings for tuning knobs and desired processing statevalues. In one configuration, the model is accessed from a modeldatabase.

In some embodiments, as a model from the model database is used, amachine learning process makes adjustments to the settings for thetuning knobs of the model to improve settings to the specific plasmareactor to achieve the desired processing state values. This causes themodels in the model database to be refined and improved over time.

In some embodiments, the method includes updating the models in themodel database based on the adjustments made by the machine learning.

In some embodiments, the machine learning uses as input sensitivity datafor the sensors of the plasma reactor, such that the producedcompensation vector includes adjustments that are moderated based onsensitivity data.

In another embodiment, a system for controlling processing state of aplasma process of a reactor is disclosed. The plasma reactor has aplurality of tuning knobs for making settings to operational conditionsof the plasma reactor. A plurality of sensors of the plasma reactor isincluded, and each of the plurality of sensors is configured to producea data stream of information during operation of the plasma reactor forcarrying out the plasma process. A controller of the plasma reactor isconfigured to execute a multivariate processing that is configured touse as input desired processing state values that define intendedmeasurable conditions within a processing environment of the plasmareactor and identify current plasma processing values. The multivariateprocessing uses a machine learning engine that receives the desiredprocessing state values, receives data streams from the plurality ofsensors during processing of the plasma process, receives sensitivitydata regarding sensor signals to compensation of tuning knobs, andreceives reactor wall surface dynamics for use by a phenomenologicalmodel that defines plasma dynamics within the processing environment interms of said data streams produced by said plurality of sensors of theplasma reactor.

The machine learning engine is configured to identify current processingstate values used to produce a compensation vector. The compensationvector defines differences between the desired process state values andthe current processing state values. The controller is furtherconfigured to execute compensation processing that transforms thecompensation vector expressed in terms of measured conditions within theprocessing environment to changes of specific one or more of the tuningknobs of the plasma reactor. The controller is configured to instructregarding changes to the tuning knobs of the plasma reactor to cause achange in the measurable conditions of the processing environment of thereactor.

In some embodiments, the machine learning engine is configured toperiodically receive measured substrate performance data regarding oneor both of etch rate measurements or monitor wafer measurements. Themeasured substrate performance data is used to make adjustments to thedesired processing state values, which in turn cause adjustments to thecompensation vector and the resulting changes to said one or more of thetuning knobs.

In some embodiments, the machine learning engine is configured toperform verification of the current processing state values with realdata obtained from one or both of etch rate measurements or monitorwafer measurements.

In some embodiments, the system executes a plasma reactor seasoningphase that uses non-production substrates. The plasma reactor seasoningphase is monitored by the controller by executing the multivariateprocessing to identify when the current processing state values arewithin a bound that enables adjustment of the tuning knobs to place theplasma reactor in a state that is ready for processing productionsubstrates and enables discontinuing of the plasma reactor seasoningphase.

In some embodiments, the system executes a production phase that usesproduction substrates. The controller executes the multivariateprocessing to identify when the current processing state values arewithin a bound that enables adjustment of the tuning knobs to compensatefor drift in the plasma process. The compensation for drift occurringmultiple times during said production phase, and the adjustments in thetuning knobs are calculated to move the processing environment closer tothe desired processing state values as measured by the plurality ofsensors.

Other aspects will become apparent from the following detaileddescription, taken in conjunction with the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

The embodiments may best be understood by reference to the followingdescription taken in conjunction with the accompanying drawings.

FIG. 1 illustrates an example plasma reactor, which may be used inconjunction with a controller, to process substrates.

FIG. 2 illustrates a general framework for utilizing multivariateprocessing to monitor and provide dynamic feedback to tuning knobsduring processing of a reactor, in accordance with one embodiment.

FIG. 3 illustrates an example of generating and updating models used forinitiating the processing of substrates in reactors, in accordance withone embodiment.

FIG. 4 illustrates a diagram of a system where the controller is used toidentify an initial model for processing a substrate, in accordance withone embodiment.

FIG. 5 illustrates a general example of using multivariate processor inorder to make adjustments to tuning knobs.

FIG. 6 illustrates an example flow where data streams from sensors areprovided to the multivariate processor.

FIG. 7A illustrates a flow diagram used to represent dataflow associatedwith operating the multivariate processing, in accordance with oneembodiment.

FIG. 7B illustrates an example implementation of machine learning tomaintain processing state, in accordance with one embodiment.

FIG. 8 illustrates an example process operation, in accordance with oneembodiment.

FIG. 9 illustrates an example of method operations that can be performedfor verification operations and drift control operations, in accordancewith one embodiment.

FIG. 10 illustrates an example of an operation of bringing a reactor upafter reactor seasoning operation, in accordance with one embodiment.

FIG. 11 illustrates another embodiment, where the reactor may be cleanedor maintained in operation.

FIG. 12 is a simplified schematic diagram of a computer system forimplementing embodiments.

DETAILED DESCRIPTION

The following embodiments describe methods, devices, systems, andcomputer programs for monitoring plasma processing systems, and inparticular, plasma processing tools used to process semiconductorsubstrates, e.g., wafers.

In one embodiment, methods and systems are provided to address thecomplexity of tuning plasma reactors during processing to achievedesired processing performance and maintain this performance over timeas the plasma reactor experiences physical changes, e.g., due toparticle and/or material buildup on surfaces and chamber walls, andconsumption or wear of plasma exposed chamber consumable parts. Further,there is a need for methods and systems to enable monitoring chamberconditions during post-clean operations, e.g., wet cleans, to enableidentification of when chambers are exhibiting characteristics thatconfirm completion of seasoning processes.

In one embodiment, the complexity associated with such monitoring ofprocessing state, identifying when seasoning processes are complete, andadjustment for process drift during production processing is overcome byimplementation of data analytics. The data analytics uses data streamsfrom different sensors present (or new sensors incorporated) in a plasmareactor. Data is then analyzed to provide substantial real-timeinformation about a plasma reactor's processing environment. Throughthis information it is possible to define deviations from an idealbehavior and henceforth derive a set of compensation values that can beapplied to tuning knobs of the plasma reactor to correct for thatdeviation.

In one embodiment, in addition to comparing current processing states todesired processing states for a plasma process type and a plasma reactortype, a machine learning engine is configured to learn from pastprocessing, which produces adjustments and refinements to the desiredprocessing state values. In one embodiment, the machine learning engineoperates a mathematical model that is refined over time and is able tolearn and correct not only the desired processing state values but alsothe compensation variables and its magnitude which upon translation intophysical variables can be used as tuning knobs to physical controls,values, settings of a plasma reactor.

In one embodiment, aspects of the disclosed embodiments define what aprocess engineer wants the processing state of the plasma to be when aproduction wafer is introduced into the plasma reactor. Broadlyspeaking, the processing state is the desired processing state, whichare measurable conditions within a processing environment of the plasmareactor. The conditions are, for example, measured by a plurality ofsensors of the plasma reactor, which during processing, produce datastreams. Each data stream, for example, can provide values read for aparticular condition over time, and the changes in the values representchanges in said condition.

In one embodiment, the desired processing state values of the reactordefine the desired state of operation for the plasma reactor when awafer is to be introduced. For example, if the plasma reactor has justundergone a wet clean, the plasma reactor is put through a seasoningphase where seasoning wafers are processed until the plasma reactorreaches the desired processing state, or in one embodiment, when theplasma reactor tuning knobs are adjusted to shift the current processingto the desired processing state. In one embodiment, processing state isdefined as one or a combination of ions, radicals, electrons and neutralfluxes at a wafer plane with given wall boundary conditions. Theseconditions, in one embodiment, are detected by sensors of the plasmareactor.

Processing state can be defined as a spatial variable, but in oneembodiment it can be defined at an arbitrary point in the plasmareactor. Correlating processing state at this arbitrary point toon-wafer results (e.g., during process verification) eliminates the needto deal with spatial dependence of processing state inside the plasmareactor. By way of example, spatial variation in processing state can bedealt with sensor data from different spatial positions inside thereactor. It is believed that information on “etching state” of thereactor (if the process is an etch plasma process), is contained in datastreams from its sensors. Any particular data stream might not have allthe information but mathematical combination of different data streamscan identify the “processing state” of the plasma reactor.

This is a unique way of identifying “processing state” defined in termsof a mathematical model of sensor output from the plasma reactor. In oneembodiment, the “processing state” of the reactor can be described in amathematical framework, thereby applying it to any reactor with sensorsoutput characterizing reactor's basic features.

Once a “processing state” of the plasma reactor is defined in terms ofits sensor outputs, it is possible to continuously monitor the etchingstate in real time and compare it with a desired “processing state”.Comparison between current and desired “processing state” will generatea “compensation vector” which can be transformed into reactor levelvariables (i.e., knob ID and amount of change to said knob) through asuitable mathematical transformation. Reactor level variables, in thisexample, are therefore the tuning knobs on the plasma reactor, e.g., gasflows, pressure, temperature, etc. Through compensation vectors, thetuning knobs on the reactor can be compensated to achieve the values forthe desired “processing state” in the plasma reactor.

In one embodiment, data streams from a reactor are used in a whole newperspective and thereby make them useful for the purpose of chambercontrol through the use of machine learning. FIGS. 1-12 below willprovide examples of how information may flow to enable machine learningin the context of plasma reactors. Broadly speaking, the machinelearning will enable efficient process calibration to set-up plasmareactors after wet clean operations, enable efficient identification ofwhen a plasma reactor has completed its seasoning operation, and dynamicuse of machine learning to correct for process drift during productionwafer processing.

It will be apparent, that the present embodiments may be practicedwithout some or all of these specific details. In other instances,well-known process operations have not been described in detail in ordernot to unnecessarily obscure the present embodiments.

FIG. 1 illustrates an example plasma reactor 100, which may be used inconjunction with a controller 120, to process substrates 102. Thesubstrates may be, for example, semiconductor wafers, flat paneldisplays, or any other type of substrate that can be or may be processedusing a plasma process. The controller 120, is configured to executemultivariate processing 130, which implements machine learning in orderto dynamically adjust the processing state of the reactor 100, based ona desired processing state. It should be understood that the plasmareactor 100 is only one type of reactor that can benefit from themultivariate processing 130 that can be executed by a controller 120 ora processing computer interfaced with the controller 120.

As shown, the plasma reactor 100 is a capacitively coupled plasma (CCP)etching chamber, which uses an RF (radio frequency) source 112 to powera bottom electrode 104. The top electrode 106 is coupled to ground, andconfinement rings 108 are used to maintain plasma 121 in a processingregion over the surface of the substrate 102. This chamber also includesa liner 110, which protects the chamber wall surfaces from plasmaparticulate buildup, and allows for efficient cleaning. A focus ring 107is shown surrounding the substrate 102 and the bottom electrode 104.Source gases 116 are, in one embodiment, delivered into the plasmareactor 100 through a showerhead disposed in or adjacent to the topelectrode 106. One or more pumps 114 are used to adjust the pressurewithin the plasma reactor 100, processing gases during operation.

The CCP etching chamber is therefore only one example of a plasmareactor 100, which can benefit from utilizing multivariate processing130 to achieve efficient compensation of tuning knobs so that desiredprocessing states are achieved and/or maintained. Without limitation,other types of plasma chambers can include deposition chambers thatutilize different types of deposition processes, other types of etchingchambers, such as inductively coupled plasma (ICP) etching chambers, andthe like. Anyone of these chambers may be controlled by a controller 120or a computer, so as to adjust system controls 124 of the plasma reactor100. The system controls 124, in one embodiment, represent controlsprovided to one or more tuning knobs 134. The plasma reactor 100 mayalso be associated with a plurality of sensors 132. In some embodiments,the sensors will vary depending on the structure of the plasma reactor100, or additional sensors may be added to the plasma reactor 100 tocapture specific types of data from the plasma 121 during processing.

As shown, the sensors 132 may include one or more of an optical emissionspectrometry (OES) sensor, a pressure sensor, a voltage sensor, acurrent sensor, a temperature sensor, a flow rate sensor, frequencysensor, a power sensor, a metrology sensor, and combinations of two ormore thereof. As an example, the following table A illustrates exampleinformation that can be obtained from various sensors of a plasmareactor.

TABLE A Data Stream Containing Information OES Plasma Species PlasmaDensity Gas Temperature Gas Density Pressure Gas Density Gas TemperatureVAT Valve Change in position indicates Number density change GasTemperature Confinement Ring position Change in position indicatesNumber density change Gas Temperature Match Capacitor Position PlasmaImpedance change Could be correlated with OES to infer plasma Chemistrychange Impedance Magnitude/Phase Contains Information on chamber stateHeater Duty Cycle to Gas heating or Density change maintain S.P Can becorrelated to with OES or Electrical signals Power required to maintainChange in Plasma Density fixed Voltage Forward and Reflected Loss ofDeposited Power Power Could be correlated with OES and Electricalsignals to indicate shift Harmonics in Frequency Indication of shift inPlasma state

It should be noted that different types of plasma reactors will havedifferent types of sensors, and it is possible to add additional sensorsbeyond those listed herein, which are only provided by way of examples.Furthermore, it should be understood that this information need not dealwith absolute values. Thus, delta changes are of interest in themultivariate processing that uses said machine learning. Consequently,even small amplitude changes may be statistically evaluated.

Also shown is example tuning knobs 134, which additionally can be varieddepending on the type of plasma reactor. Example types of tuning knobscan include one or more of controls to adjust gas flow meters (e.g.,MFCs), controls to adjust power settings, controls to adjust temperaturesettings, controls to adjust physical gap separations between top andbottom electrodes of the plasma reactor, controls for adjusting anelectrostatic chuck (ESC) temperature or operation, controls foradjusting bias power settings, controls for setting chamber pressures,controls for setting frequencies of one or more radio frequencygenerators, controls for setting run time of specific recipe operations,controls for setting pumping rates of vacuum, controls for settingduration of gas flows, controls for gas partial pressure in a recipe,controls for setting monitoring algorithms, controls triggeringborescope inspections, controls for setting or determining intervalsbetween clean operations, or combinations of two or more thereof. Asnoted, different types of plasma reactors will have different types oftuning knobs, and it is possible to add additional tuning knobs beyondthose listed herein.

In one embodiment, the controller 120 may execute the multivariateprocessing 130 in order to place the plasma reactor 100 into productionservice. This operation may be required after a chamber cleaningoperation, wherein the chamber is opened to atmosphere and thoroughlycleaned and/or parts removed and replaced. Sometimes, this type ofcleaning is referred to as wet cleaning, since the plasma reactor isopened and subjected to various types of cleaning and or conditioningoperations. The requirement to clean the chamber periodically duringservice is necessitated because plasma processing by its very naturewill produce particulates and byproducts that may be adhered to thesurfaces of the interior regions of the reactor, and the surfaces mustbe cleaned in an effort to prevent excessive process drift.

If excessive process drift occurs, the performance of an etch operation(or deposition operation) will vary, and may not produce the sameresults that are required for a specific process. Before the drift haschanged the process results to levels that are not acceptable, chambersare typically programmatically shut down and required to undergo a wetclean. After the wet clean is performed, the chamber needs to go througha seasoning operation. The seasoning operation utilizes substrates thatare configured to approximate the type of process being performed withproduction wafers. During the seasoning operation, etch processes areperformed (or deposition processes), and this very process will causeparticulates and material to adhere to the surfaces within the processchamber.

Without utilizing the multivariate processing 130 described herein,typical processing would require operators to simply run seasoningoperations for several hours. Because it was not possible to accuratelydetermine when the chamber was adequately seasoned, common practice hasbeen to simply run the seasoning operation for more hours than wouldpossibly be needed. Of course, this introduces substantial delay inmaking the plasma reactor available for processing production wafers. Inaccordance with one embodiment, the multivariate processing 130 isconfigured to utilize machine learning to compare desired processingstate values detected from data streams captured by sensors of theplasma reactor, and utilize machine learning to determine whatadjustments are required to the specific tuning knobs so that thecurrent processing state values match or closely approximate the desiredprocessing state values.

This process can be performed after a wet clean operation, which canidentify when the chamber is ready to be placed into production, withoutwasting excessive time running seasoning wafers, when the wafer isindeed already ready for production use. When the plasma reactor isplaced into production, specific recipes 122 can be set, so as to definethe starting parameters of the plasma processing. The startingparameters can identify initial settings for the tuning knobs 134, andother parameters associated with placing the plasma reactor in acondition for receiving a production wafer. Initially, the processingstate of the plasma reactor can be identified from a model database,where a model includes desired processing state values and tuning knobsettings. The model database, in one embodiment, may be constructedinitially by experimental learning processing, which utilizes testsubstrates in order to create an initial model that is placed into themodel database.

As the process is run in production, the production processing willproduce updates, utilizing the multivariate processing, which update andrefine the model for the process. Therefore, as the process is run moretimes, the values of the desired processing state and the correspondingtuning knobs are also refined and updated. This produces a type ofcontinuous learning feedback, which over time improves the performanceof the system. Thus, sensor data from the sensors 132, as received fromsensors of the plasma reactor 131 will be producing sensor data streams136, which are fed to the multivariate processing 130. If themultivariate processing 130 determines that an adjustment is needed tothe settings of the specific one or more tuning knobs 134, compensation138 is propagated to the actual tuning knobs, to effectuate a change 140in the processing. This feedback operation ensures that the plasmaprocessing environment in the plasma reactor 100 is achieving thedesired processing state even when the conditions within the plasmareactor are changing (e.g. particulate and/or material buildup on thewalls and/or consumable part where is occurring).

FIG. 2 illustrates a general framework for utilizing multivariateprocessing 150 to monitor and provide dynamic feedback to tuning knobs134 during processing of a reactor 100, in accordance with oneembodiment. The multivariate processing 150, in one embodiment, utilizesmachine learning engine 180, which functions to take inputs from variousdata producing modules in order to determine the degree of compensationneeded based on changing conditions within the plasma reactor 100.

The compensation is provided in the form of adjustments to tuning knobs134, which modify the conditions of the plasma reactor 100, whichtherefore are calculated to produce a current processing state that moreclosely approximates the desired processing state for a specific processtype being run in the plasma reactor 100. Broadly speaking, the processtype shall refer to a type of processing operation to be performed inthe plasma reactor 100. The process type can be defined in terms of aspecific type of etching operation and its specific chemistries andparameters associated with a recipe. Similarly, the process type can bedefined for a specific deposition operation to be executed by the plasmareactor 100. In the following discussion, reference is made to etchingoperations, but it should be understood that the operations can equallybe utilized for deposition operations.

As illustrated, model processing 151 is an operation that may beperformed to generate models that characterize a process for a plasmareactor, in accordance with one embodiment. Initial model generation 152may be performed when a type of reactor or process has not beenpreviously run, and a process engineer needs to qualify the specificprocess for a specific plasma reactor. These operations can be performedby experimental testing of test wafers in the plasma reactor whenexposed to specific processing conditions and associated tuning knobsettings. Once the process has been validated by a process engineerduring this experimental testing, a model is generated in operation 154.

The model will include an identification of the process type and thereactor type. The model, as described in more detail below, may includeinformation that identifies the desired processing state as well as thetuning knob settings for the specific process. By way of example, theprocessing state values will be identified in terms of their detectablecharacteristics, e.g. by sensors of the plasma reactor. Thischaracterization of plasma state values is, in one embodiment, referredto as virtual space characteristics, because the values are not specificsettings but instead are detectable values that can be identified fromdata streams collected from sensors of the plasma reactor. As will bedescribed below, these virtual space characteristics can be transformedinto real tuning knob identifiers and tuning knob magnitudes, whichdefine which specific tuning knob will be adjusted or changed and thespecific amount to change or adjust the tuning knob or knobs.

Accordingly, the process can begin by a user (process engineer ortechnician) identifying a process set up for a plasma process inoperation 160, which acts to select a model 161 from a model database.The model, which includes the desired processing state values andinitial tuning knob settings are communicated to the multivariateprocessing 150 via 162. This defines the initial starting point of theprocess, which identifies the specific processing state values that aredesired. Data streams 136 from sensors of the plasma reactor 100 areprovided to the machine learning engine 180 of the multivariateprocessing 150.

In addition, the machine learning engine 180 is utilizing the desiredprocessing state values 170 in order to determine when based on the datastreams provided by the sensors, the current processing state values 172are not matching the desired processing state values 170. In addition,the machine learning engine 180 will be receiving periodic informationfrom etch rate analysis, which may be performed after one or moresubstrates are tested using a metrology tool. Similar processes can beperformed with a monitor wafer, which is configured to approximate thetype of processing desired to be executed by the plasma reactor 100. Ineither operation, etch performance verification 174 can be performed andprovided as periodic data 171 to the machine learning engine 180.

This allows the machine learning engine 180 to determine when thedesired processing state values 170 should be adjusted, as the trueperformance of the plasma reactor 100 is no longer matching the originaldesired processing state 170. As such, the machine learning engine 180may be dynamically adjusting the desired processing state 170 based onits periodic validation operations, e.g., utilizing off-line metrologytest data that is fed back to the machine learning engine 180.Additionally, machine learning engine 180 may be provided withinformation regarding reactor wall surface dynamics 182. Thisinformation may include data regarding the inferred characteristics ofthe chamber wall surfaces, as they change during processing. By way ofexample, this data can be inferred from historical measurements of wallcharacteristics, e.g., material buildup, flaking, roughness, consumablepart usage, and other physical characteristics. This data can beinferred, as it may be provided by a model that predicts the type ofphysical changes that will occur on the reactor wall surfaces duringoperations over time. In some embodiments, this data can be dynamicallyupdated from time to time and refined based on inspection of the reactorwall surfaces, e.g. when a chamber enters a wet clean cycle.

As an optional refinement input to the machine learning engine 180 inputfrom a phenomenological model 184 is used, that approximates thebehavior of the plasma within the chamber, given the reactor wallsurface dynamics 182. The phenomenological model 184, in one embodiment,is used to approximate the nature of the chemical reactions that areoccurring within the processing volume, and associated interactions withthe reactor wall surfaces. Broadly speaking, a phenomenological model issometimes referred to as a statistical model, as it is a mathematicalexpression that relates several different empirical observations ofphenomena to each other. This relation is consistent with fundamentaltheory, but is not directly derived from theory. Thus, aphenomenological model does not attempt to explain why the variables inthe plasma (i.e., when chemical bonds break to define different chemicalspecies or when the recombine to define a different chemical form uponcoming into contact with a surface in the reactor, e.g., a chamberwall). Generally, the phenomenological model 184 is configured tocharacterize the anticipated chemical kinetics of gases in the plasma ofthe plasma reactor, and their behavior relative to the reactor wallsurface dynamics 182. These kinetics may include, for example, electroncollision reactions, wall recombination reactions, wall loss reactions,etc., for different chemistries. Thus, this model simply attempts todescribe the relationship, with the assumption that the relationshipextends past the measured values. The phenomenological model 184 isconfigured to produce input to the machine learning engine 180 that isin terms of sensor output. That is, the characterization of the plasmabehavior by the phenomenological model 184 is configured to produceinput data to the machine learning engine 180 in the form of informationsimilar to that which can be captured by a sensor coupled to the plasmareactor 100.

By way of example, the data produced by the phenomenological model 184may be characterized in the form of any one of the outputs produced bythe sensors 132, as described with reference to FIG. 1. Taking anexample of measurements of optical emission spectroscopy (OES) spectraby a sensor, e.g., an OES sensor, the output could be produced in theform of intensity (I). The phenomenological model 184 may approximatethe changes to the reactor wall surfaces and the anticipated plasmacharacteristics in the form of intensity (I). Thus, since the machinelearning engine 180 may already be receiving output from an OES sensor(e.g., in the form of a data stream), the machine learning engine 180 isprogrammed to anticipate inputs associated with intensity. Thephenomenological model 184 therefore is configured to generate input tothe machine learning engine 180 and the same form of intensity. Thus,the intensity (I) may be represented as a function of surface roughnessof the anodized chamber walls, plasma density, gas flows, etc.

Thus, it can be said that the phenomenological model 184 provides inputto the machine learning engine 180 in the form of or in terms of sensoroutput data. The example provided above with respect to OES sensor datais just one example, in the same type of modeling that can be providedfor other types of sensor data, such as sensors associated withcapacitance, voltage, current, or other measurement characteristicsproduced by actual sensors that are coupled to the plasma reactor 100.

Machine learning engine 180 is also configured to receive as input datathat defines sensitivity of sensor signals 192 with respect tocompensation values for specific tuning knobs. The sensitivityinformation can be obtained from experimental testing of a plasmareactor, wherein specific conditions of the reactor are modified and thesensitivity can be quantified. The sensitivity, for example, relates tospecific tuning knobs that can be modified and changed for the plasmareactor 100, and the resulting sensitivity associated with changes tothe settings of the knobs.

For example, changing a specific value setting on a particular knob mayhave more dramatic response to etch rate (ER) than changing anothervalue setting on another particular knob. As a further example, plasmareactors can be categorized or associated with predefined sensitivityprofiles for specific knobs, and the identification of the sensitivityvalues for each of the specific tuning knobs can be experimentallydetermined. For instance, in some cases the sensitivity slope for etchrate as pressure is increased can have a slope of about 2%, while anadjustment to source power can have a slope of about 8%. In someembodiments, the etch rate will vary based on temperature in accordancecalculated distribution function, which may increase as the temperaturegoes up and then decrease at a certain point. In addition to etch rate,which is only one parameter that characterizes the sensitivity ofchanges made by specific tuning knobs, other types of metrics that canbe characterized include, for example, changes to OES measurements inresponse to specific changes in pressure, source power, gas flows,electrode separation positions, etc.

Still further, other measurable parameters that can be affected bychanges in specific tuning knobs can include, for example, changes incapacitance position between electrodes, changes flow rates, and othermeasurable parameters that can be captured by sensors associated withthe plasma reactor 100. Thus, for every sensor output, the sensitivitycharacterization can be performed in order to determine how eachspecific change to a different tuning knob will affect the resultingmeasurements detected by the specific sensors, and associatedsensitivities. In one embodiment, it is possible to perform sensitivitytesting on a chamber by varying one or more tuning knobs at a time, andthen measuring a plurality of outputs from the various sensors. Thisprocess can be repeated for any number of tuning knobs, whilesystematically collecting the variability for each of the measurementsdetected by the sensors of the system.

Accordingly, the sensitivity of etch rate associated with varying anynumber of specific tuning knobs must be known in order to prevent themachine learning engine 180 from generating a compensation vector 194that prescribes too much or too little changes to specific tuning knobs.

The machine learning engine 180 is therefore configured to receive thedefined sensitivity of the sensor signals 192 in operation 182 withrespect to the compensation values that are to be applied to the tuningknobs 134. As mentioned above, the machine learning engine 180 isconfigured to produce current processing state values 172, which arecompared to the desired processing state values 170 in order to identifyand produce a compensation vector 194, which is processed incompensation processing 190. Compensation vector 194 is then processedthrough a transformation process 186 in order to produce compensationvalues 198. The transformation process includes converting theprocessing state value differences, which hold the information necessaryto identify which specific tuning knobs 134 will be changed or adjusted,and the magnitude of such change or adjustment. The transformation 196,is therefore a conversion formula that converts the compensation vectorvalues, which are in a virtual space (i.e., characterized in terms ofsensor output values), to compensation values 188 that are in the realspace (i.e., characterized in terms of real changes to one or more ofthe tuning knobs 184).

In one embodiment, the compensation values K(r, t), are associated witha bound definition 197. The bound definition 197 identifies the amountby which the compensation values should be allowed to change in thegiven plasma reactor 100. By way of example, if a compensation value isoutside of the bound definition 197, then the system should notimplement that change. That is, the bound definition 197 acts as asafety measure to prevent making changes to tuning knobs 134, which maynot produce the desired result or where such change would possiblyproduce a process modification that is known to not be desired withinthe specific plasma reactor or for the process being performed on asubstrate.

Still with reference to FIG. 2, data streams 136 from the plasma reactor100 will be utilized to calculate 192 Sensitivity Coefficient {rightarrow over (S)}(t) with respect to change in Tuning Knobs {right arrowover (K)}(r, t) where r is the position and t is time. In oneembodiment, {right arrow over (S)}(t) can be calculated in a regularrecipe or a special recipe can be designed to calculate {right arrowover (S)}(t) so that more insights can be achieved in Processing State{right arrow over (P)}(r, t) of the reactor. {right arrow over(S)}(t)_(i), where i represents number of signals, will be classifiedand correlated in order of relevance to represent Processing State ofthe reactor. For example, capacitor tuning position in a match system(i.e., coupled to an RF power source) could be correlated with OpticalEmission Spectrum (OES) from the plasma and can be correlated together.In a similar way we can define different orders of correlation, withhigher order defining strong correlation and lower order defining weakcorrelation of sensor signals.

As mentioned above, a phenomenological model 184 processing mayoptionally be implemented to describe plasma interactions happening atthe reactor walls which control plasma properties. The phenomenologicalmodel 184 will be expressed in terms of Data Streams coming from thereactor. Thus, this data will be a lumped parameter model with someinsights into physics of reactor level processing.

Input from Sensitivity Coefficient {right arrow over (S)}(t), directdata Streams 136, monitor wafer and/or etch rate R(t) data 174 andphenomenological model 184 are fed to the machine learning engine 184.The machine learning engine generates a statistical model based on theinputs it receives. The statistical models are generated in real-timewith input that comes into the machine learning engine 180. Astatistical model is created in virtual space with all inputs that theengine receives. The desired Processing State {right arrow over (P)}(r,t) values will be defined through this model. This statistical modeldefining {right arrow over (P)}(r, t) will thus be the blue print of thereactor for a given process. A single reactor can have multiple {rightarrow over (P)}(r, t)_(i) representing different processes that can berun on the reactor, where i represents a number of processes.

Through an extension of this definition a reactor should be able tochange its Processing State {right arrow over (P)}(r, t)_(i) and machinelearning engine 180 will generate compensation vectors to change fromone processing state to the other. Through another extension of thisdefinition, machine learning engine 180 will define processing state{right arrow over (P)}(r, t)_(i) and henceforth can be utilized to dochamber matching within a fleet of chambers by defining compensationvectors for each chamber which will be unique for a given chamber. Thiswill ensure that after application of compensation vectors to eachchamber for a given process, the chamber will be in a same processingstate within noise level defined. This approach of machine learning inchamber matching will reduce the cost of increasing precision andaccuracy on hardware subsystems which can be a significant cost saver.

In one embodiment, through a calibrated set of experiments, the modelcan be taught to define processing state of the reactor. Different typesof supervised machine learning models can be utilized for this purpose.This step, in some embodiments, is referred to as the learning stepwhere the machine learning engine 180 is taught what the desired stateis, what are the bounds, signal to noise, etc. Through an application ofsuitable algorithm the learning step can be automated.

As mentioned above, machine learning engine 180 will take all inputsfrom data streams 136 and will classify them in order of relevance asapplicable to the model. This means that some models can have more datastreams defining its entirety while other models can be described byfewer number of data streams (e.g., where each sensor produces itsrespective data stream).

In one embodiment, during normal operation of the reactor the machinelearning engine 180 will constantly monitor data stream 136 and refineitself within bounds to be able to improve its precision in defining thedesired processing state of the reactor. By way of example, the desiredprocessing state {right arrow over (P)}(r, t) will be constantlymonitored during operation through machine learning engine 180 and anydeviation will be captured, and machine learning engine 180 will correctfor the deviation and in the process will generate compensation vectors194. As mentioned above, compensation vectors 194 are defined in virtualspace.

This means that the compensation vectors are defined in terms ofmeasured sensor output characteristics that define the currentprocessing state values. Compensation vectors will be accepted bymachine learning if they are within bounds as defined by the user orfrom experience of knowing setting bounds. A transformation functionwill be applied in operation 196, wherein the compensation vectors aretranslated or converted into a tuning knob compensation vector {rightarrow over (K)}′(r, t) 198 that can be applied to the reactor. Thecompensation vector {right arrow over (K)}′(r, t) can be applied totuning knobs 134 to bring it back or maintain the desired processingState {right arrow over (P)}(r, t). In one embodiment, implementation ofmachine learning to maintain the processing state can be done in realtime, periodically on a schedule or upon user input or programmed input.In one embodiment, the processing state can be checked just beforeprocessing a wafer.

A number of machine learning algorithms may be used to process themonitored/collected data streams, and the selection of a specificalgorithm may depend on a number of factors or tradeoffs. One factor toconsider is the speed required to process the multivariate data andproduce data used for the compensation. Another factor to consider isthe accuracy of the resulting data. In light of recent significantadvances in computing power, very complex multivariate data is able tobe processed almost in substantial real-time. In some implementations,special purpose as well as general machines are now being used toprocess large amounts of data, often referred to as “big data.” In somecases, cloud processing may also be used, e.g., such as cloudinfrastructure providers that offer elastic compute nodes that scalebased on processing demand or need. The data streams obtained from thesensors of a plasma reactor can be referred to a type of big data, andsuch data may be continuously and/or periodically generated duringprocessing operations (and used to refining models). In someembodiments, data from multiple processing sessions can be collected andsaved to storage, and accessed for off-line learning. Information andrelationships learned from this off-line learning can also be usedduring real-time processing of specific recipes on specific plasmareactors.

There are several known machine learning algorithms that may be used.Without limitation, such examples may include linear/nonlinearregression, stepwise regression, decision tree learning (e.g., CART,Random Forest, Boosted Trees, etc.), association rule learning,artificial neural networks, deep learning, inductive logic programming,support vector machines (SVM), clustering, Bayesian networks,reinforcement learning, representation learning, similarity and metriclearning, sparse dictionary learning, etc. It should be understood thatrecitation of specific machine learning algorithms should not be viewedas a restriction to any one example. As newer and more advancedalgorithms are disclosed in the art of machine learning algorithms, itis envisioned that such advances can equally be used to handleprocessing of received plasma reactor sensor data and such data can beused to characterize models that provide insights into behavior andoperation of the system. Such insights can thus be used to generatecompensation data that is used to tune the plasma reactor so that theproduction of substrates does not stray or drift during prolongedprocessing of substrates.

With the above in mind, it is believed that artificial neural networkalgorithms of machine learning may be used to process the input datareceived from the data streams and the processing state values togenerate compensation via tuning knobs of the plasma reactor. Anartificial neural network (ANN) learning algorithm, is sometimesreferred to as a “neural nets” (NN), and it is a learning algorithm thatis analogized to the structure and functional aspects of biologicalneural networks. Computations may be structured in terms of aninterconnected group of artificial neurons (e.g., nodes) and thenprocessing information using a connectionist approach to computation. Inone embodiment, a neural network usable in the context of data streamsrepresenting monitored plasma conditions may be in the form ofnon-linear statistical data. These nodes are used to model complexrelationships between input data streams and other inputs described withreference to FIG. 2, and the desired processing state. Patterns in thedata may be found and used to produce statistical decision outcomes.

Thus, one type of machine learning includes neural network processing,which commonly uses a decision tree that is defined from nodes of theneural network. Inputs to the nodes of the neural network may be thedata streams, and the different inputs to the nodes may be associatedwith a weight. The weight is used to determine importance of specificinput connections. Further, a neural network may have a plurality ofinput nodes and one or more layers of node (sometimes referred to ashidden layers). The initial generation of the decision tree, in oneembodiment, includes initially assigning random connection weights tothe inputs to each of the nodes in the tree. The connection weights arethen refined and learned using a known process referred to asbackpropagation. In one embodiment, the desired processing state valuesrepresent the desired outputs of the decision tree and the input nodesreceive the current processing state values. Using the random weights,output paths are calculated. These output paths that are calculated arethen compared to the outputs associated with the desired processingstate values.

The difference between the calculated outputs with the random weightsand the outputs associated with the desired processing state values arereferred to as the error in the network. Knowing this error,backpropagation is used to adjust the connection weights in an attemptto produce smaller errors. The adjusting uses a formula that is based onthe old weights, the node input values, the error and a learning weight.This process of weight adjustment is repeated until all nodes areassociated with updated weights. This process is used to identify whichnodes are most responsible for the errors in the output, and theirweights are adjusted the most. This process is continually repeateduntil the decision tree of nodes has been fit with weights that bestdefine the significance or insignificance of specific nodes in the tree.During this process, specific one or more of the data streams that arecausing the errors can be identified and added to the compensationvector. The compensation vector, therefore, represents the values thatmust change in order for the current processing state to match or bestresemble the desired processing state. In some embodiments,pre-processing is required to define a model, which includes the desiredprocessing state values. This process is described, for example, withreference to FIG. 3. The process of defining a model may, in some cases,take time to establish the desired model, and this processing can bedone off-line. Once the model is generated, the model represents thestarting or initial model. Over time, as the model is used in productionprocessing, the model can continue to be refined using the machinelearning. In operation, the data sets used to define the models may besaved to storage that is accessible by one or more processing machines.

In some configurations, the processing machine that executes machinelearning may be the controller of the tool itself or can include one ormore networked computers. Sometimes, the processing necessary toconstruct the models may require substantial processing power, and theworkloads may be distributed to more than one computer or virtualmachines. Additionally, processing of such large data sets, e.g., bigdata, may be performed in cloud processing systems. Cloud processingsystems may be provisioned with high processing power virtual machines,which can share the processing load to reduce processing delays. Oncethe models are constructed and saved to the model database, the modelscan be used in real-time by the plasma reactor. The processing and useof the model may be via the controller of the plasma reactor or via oneor more other networked computers (local or in the cloud).

FIG. 3 illustrates an example of generating and updating models 200 usedfor initiating the processing of substrates in reactors, in accordancewith one embodiment. As shown, when a reactor is new or the reactor hasnot been previously characterized, the reactor can be characterized todefine a model through a plurality of experimental learning processes202. In these processes, test substrates 204 can be processed by thereactor. The processing of the test substrates 204 occur based on aplurality of knob settings 206 and these experiments can be processedfor a plurality of different process recipes 208. Sensors 210 can beused to monitor the reactor during the learning processing, so as todefine and generate processing states 212.

This information can then be used to generate an initial model 220,which can be added to a model database 230. As shown, the model databasewill include a plurality of models 230 a-230 n, and each model 230 willcharacterize processing state {right arrow over (P)}(r, t) values,representing the desired processing state values 234 and thecorresponding tuning knob 232 settings, which are anticipated to producethe desired processing state 234 for use in beginning processing of asubstrate. As discussed above, when the processing first begins, a modelis obtained from the model database in operation 154, which representsthe beginning state for processing of a substrate. During theprocessing, any drift is accounted for by adjustments made to thecompensation values to the tuning knobs 134, which further act toprovide model updates 156 to the models.

This processing is shown in FIG. 3, where during real-time processing204, fabrication or production substrates 204 a are processed inreactors, and the resulting processing is continuously executed by themultivariate processor 150, which provides machine learned updates 156to the models 230. Accordingly, as a model is continuously used in aspecific reactor for a specific process, that model will be refined overtime for that reactor, and updates to the model can be saved to themodel database. Thus, when subsequent processing is done with the samereactor, the same model can be accessed, which can be specifically tunedand refined for that specific reactor and process. Accordingly, overtime, the models in the model database 230 will be continually updatedand refined by the changes to the model, per the updates made to theprocessing state by the machine learning.

FIG. 4 illustrates a diagram 300 of a system where the controller 120 isused to identify an initial model for processing a substrate, inaccordance with one embodiment. In this example, the controller 120 willidentify a reactor 302, and also identify a process 304. Thisinformation is used by the controller 120 to identify the reactor andprocess from a database 320, which includes information related tomultiple types of reactors and processes associated with each type ofreactor 322 a-322 n. Once the reactor and the process have beenidentified, the model database 230 can be accessed to identify aspecific model, which in this example is model 230 j. Model 230 jincludes the desired processing state P(r, t) and tuning knobs forachieving the desired processing state. As mentioned above, the desiredprocessing state is the initial processing state that the multivariateprocessing will try to match based on the data streams received from thereactor 100, and based on verification and/or confirmation with etchrate or monitor wafers, adjustments to the desired processing state canbe made so that the machine learning engine 180 can identifycompensation vector(s) that will achieve the currently desiredprocessing state.

The compensation vector, as noted above, will be transformed into actualcompensation values 198 that can be applied to the tuning knobs 134,based on the ballot definition 197. In alternative embodiments, insteadof requiring the controller 120 to identify the initial model 230 j, atechnician or engineer setting up a process can identify the model andprovide it as input as a starting point. As mentioned above, the modelscan be used as an initial point in order to season a chamber aftercoming out of a wet process, and then enabling the determination thatthe chamber is ready for operation without unnecessarily proceeding withseasoning operations. In one embodiment, if it is determined that thechamber can be adjusted within a certain bound of setting adjustments tothe tuning knobs, the compensation vector can be applied to generatechanges to the tuning knobs that are applied to the plasma reactor, inorder to make it ready for processing production wafers.

Once production wafer fabrication is ready for the reactor, theprocessing state used to achieve or ready the processing chamber can beused, along with the associated tuning knob settings. In this manner,when the production wafer is initially introduced into the reactor, itis believed that the reactor will be ready to process that productionwafer and achieve substantially the same processing state as desired. Ifthe processing state drifts, as the wafer or wafers are processed in thereactor, the multivariate processing 150 can apply compensation vectorvalues so that the tuning knobs 134 can adjust the processing andachieve the desired processing state.

FIG. 5 illustrates a general example of using multivariate processor 150in order to make adjustments to tuning knobs 134. The process generallyincludes a feedback system that allows for reading of data streams 136from sensors of the plasma reactor 100, processing the data streams fromthe sensors 136 in a multivariate processor 150 that includes machinelearning, and then apply changes to settings of the tuning knobs 134,which are applied to the plasma reactor 100. This feedback loop ensuresthat changes made to the plasma reactor are tracked to the actual datastreams being sensed by the sensors associated with the plasma reactor.That is, changes made to the tuning knobs 134 are made to correspond tothe characteristics of the plasma measured within the processing spaceof the plasma reactor 100. In this manner, it is possible to makeadjustments to the tuning knobs 134 to maintain or achieve a processingstate within the plasma reactor 100 that is desired for the specificreactor and the specific process being implemented.

FIG. 6 illustrates an example flow where data streams 136 from sensorsare provided to the multivariate processor 150. Once the multivariateprocessor 150 has identified the necessary changes required to bring theplasma reactor back to the state that is consistent with the desireprocessing state, the multivariate processor 150 will produce acompensation vector 194. Compensation processing 190 therefore includesreceiving the compensation vector 194 from the multivariate processor150. The compensation vector 194 is represented by metrics that aredescriptive of measurement values obtained from the sensors of theplasma reactor 100.

Therefore, these metrics are not directly relatable to actual changesneeded to be made to the tuning knobs 134. In one embodiment, atransformation function 196 is performed in order to convert the data inthe compensation vector 194 into compensation values 198. Thistransformation can be performed using a lookup table of conversioninformation which is mapped to correspond to compensation vector data tocompensation values that identify a specific tuning knob and a magnitudefor adjusting a setting of the tuning knob.

By way of example, it is possible that the transformation function 196can identify that only a certain number of tuning knobs need to beadjusted, such as more important or higher relevant tuning knobs. Tuningknobs that will not affect or cause much change to the processing statemay not be adjusted at all. Therefore, in addition to simply translatingthe compensation vector values 194 into compensation values 198, thetransformation function can eliminate certain knobs from being adjusted.Once the compensation values 198 have been identified, these values aretransferred to the tuning knobs 134 of the reactor 100. The tuning knobsof the reactor can include settings, valves, controller instructions,changes, inputs, and the like. Any number of these tuning knob settingscan be commanded or instructed by the controller 120 or can be set by anoperator or set by manual operation.

FIG. 7A illustrates a flow diagram used to represent dataflow associatedwith operating the multivariate processing 150′, in accordance with oneembodiment. In this example, the plasma reactor 100 is shown providingmultiple outputs from different sensors. The different sensors willtherefore produce data streams 136 during processing. To initiateprocessing, a model is selected in operation 154, which includes theprocessing state and the knob settings. As mentioned above, the modelincluding the processing state and knob settings can be obtained from amodel database, which may be accessible by a controller of the plasmareactor or a connected or networked computer. The model will include thedesired processing state 170, which is defined in the virtual space.

Again, as mentioned above, the virtual space represents data that isdescriptive of or representative of sensor output found in the datastreams 136. The current processing state 172 is derived from; at leastdata obtained from the data streams 136 from the sensors. As mentionedabove, the current processing state can also include information processfrom reactor wall surface dynamics 182, and optionally thephenomenological model 184. For simplicity in describing the processingflow, the multivariate processing using machine learning will identifydifferences between the current processing state 172 and the desiredprocessing state 170, by way of an operation of comparing of processingstate 400.

Verification operation 402 can also be used by the multivariateprocessing 150′, in order to determine whether the actual processing bythe plasma reactor 100 is in sync with the desired processing state 170.The verification 402 will therefore allow for adjustments to thecomparison of processing state 400, so that any adjustments take intoconsideration the verification 402. The output of the multivariateprocessing 150′will therefore generate compensation vector 194 in thevirtual space. The resulting compensation vector 184 will be used as anupdate 406 to the model 154, which acts as a learning process, whichupdates the desired processing state based on actual dynamics occurringwithin the processing volume and the data streams being sensed andproduced by the sensors. In this example, the compensation vectors and84, which are in the virtual space, are transformed in operation 404,where the compensation vectors are converted to the real space toidentify which knobs to adjust and the amount of adjustment.

The resulting compensation values K₁(r, t)−K_(n)(r, t) will therefore beapplied to the tuning knobs 134, which are applied to the plasma reactor100. As mentioned above, the application of the changes to the tuningknobs can be applied in various forms, depending on the specific changesto the settings of the tuning knobs. Some knobs are represented asvalves, some as digital input, some as frequencies, some as powerlevels, some as gas flow, some as electrode positioning spacing, some ascapacitor settings in matching networks, some in temperature settings,some in electrostatic chuck temperature, some in vacuum pressure, somein pumping rate, some in processing time, some in mixing ratios, andmany more settings which are custom to the specific recipe and/orreactor setup. It should be understood that these example settingscontrolled by the tuning knobs are simply examples, and many more canexist. Furthermore, reference to tuning knobs should not only be viewedas actual physical knobs, but simply as an identifier for a specifictype of setting for a specific type of control, input, or variable. Ofcourse, in some embodiments, the tuning knob may actually be a knob.

FIG. 7B illustrates an example implementation of machine learning tomaintain processing state, in accordance with one embodiment. In thisexample, machine learning engine can be used in real time. In order tokeep the processing within bound and avoid the risk of implementingsetting changes that are not needed or excessive at any one point, animplementation uses a check of the reactor processing state just beforeprocessing a wafer, as shown in FIG. 7B. In this manner, active waferprocessing can occur, followed by chamber cleaning/preparationoperations (e.g., which include reactor seasoning). A heath monitoringoperation can be performed, just prior to entering active waferprocessing, as a safety check. As can be appreciated, this strategy willensure that chamber is in right state before taking the wafer in andreduces risk of misprocessed wafers.

FIG. 8 illustrates an example process operation 500, in accordance withone embodiment. In this example, processing of a substrate in a reactorusing settings of tuning knobs approximated to achieve desiredprocessing state values is defined in operation 502. In one embodiment,processing state adjustment can be performed during productionprocessing of substrates. In another embodiment, processing stateadjustment can be performed during chamber seasoning, following a wetclean. In this example, it is possible that the processing has beeninitiated after the wet clean operation process has been performed, andthe chamber has been seasoned and made ready for processing ofproduction substrates.

During processing, data streams from the sensors are communicated fromthe reactor to a multivariate processor in operation 504. Operation 506shows the active monitoring and adjustment to tuning knobs, so as toachieve a desired processing state within the reactor. In operation 508,current processing state values are monitored by examination of the datastreams, by the multivariate processor. As mentioned above, themultivariate processor can have other inputs, which are useful to themachine learning in order to more accurately identify values for thecompensation vector.

In operation 510, the current processing state values are compared to adesired processing state values, by the multivariate processor. Thiscomparison is preferably performed continuously during operation in realtime. In another embodiment, this comparison can be performedperiodically, or upon instruction by a computer program or by a user viamanual input. In operation 512, and adjustment to specific knob(s) ofthe reactor are applied with adjustment values derived from thecomparing operation performed by the multivariate processor. Operation514 updates the model with learned variations made to knob settings ascorrelated to the monitor data streams and the compared processing statevalues. In this manner, models are continuously updated based on learnedinformation by the machine learning of the multivariate processor forthe specific reactor and specific recipe.

In operation 516, it is determined if the process should continuemonitoring and applying adjustments. During the processing of one ormore substrates, the process can continue with the monitoring, which cancontinue to update the model with adjustments made in order to keep theprocessing state consistent with the desired processing state. Thiscontinuous monitoring and applying of settings to the knobs assist incontrolling process drift that may be occurring as more and more wafersare processed in the reactor. As mentioned above, as more wafers areprocessed in the reactor, more particulate buildup or part wear willoccur inside the processing volume. The very nature of these physicalchanges will cause drift in the resulting processing of the wafer.However, because the processing state is being monitored, these changeswill be exhibited in the detected processing state as gathered from thesensors.

Because these drift occurrences are being detected, the machine learningassociated with the multivariate processor can make adjustments to theknobs of the reactor, so as to maintain the processing by the reactor ina state that achieves the desired processing state. As mentioned above,in addition to making these real-time changes to the processing state byway of the changes to the control knobs, various verification steps canbe performed after processing of any number of wafers. This verificationcan be fed back, so as to adjust any adjustments made to the knobs, andavoid changes when the desired processing state is no longer achievingthe desired result. Advantageously, the desired processing state isupdated using feedback from the verification and/or monitoring, so thatthe adjustment by the machine learning can continue to apply adjustmentsto the control knobs in a manner that is consistent with the actualachievable results and performance of the reactor.

Furthermore, it should be appreciated that by correcting for drift, itis possible to achieve a high level of performance by the reactor forspecific process recipes, and also achieve higher levels of waferprocessing throughput from the reactor, before requiring the reactor tobe brought down for cleaning. Additionally, if the monitoring isperformed during seasoning steps following a wet clean, is also possibleto start production wafers sooner, instead of wasting time continuingwith seasoning operations that are not actually needed and are actuallyreducing production time that could be used for production wafers.

FIG. 9 illustrates an example of method operations that can be performedfor verification 600 operations and drift control operations 650, inaccordance with one embodiment. The verification operations 600 can beperformed in order to determine whether a reactor has reached a statewhere it is ready to process production wafers. By way of example, thisprocessing can be performed on a reactor during chamber seasoningoperations. Drift control 650 can be performed, for example, after thereactor has entered processing of production wafers, and changes to thetuning knobs are required to correct for drift occurring due to use ofthe reactor.

In operation 602, information regarding the process type and the reactortype is received. This information is used to identify a model from themodel database in operation 604. The model database may include aplurality of models that can be used for specific processes for a givenreactor, and may include various reactors with their own specificprocesses that have been modeled. An example of a database includingdifferent types of reactors and processes that have been modeled forthose reactors is shown in database 320 of FIG. 4. In anotherembodiment, the model may be obtained from a file, or may be input by atechnician or engineer to the controller or a computer connected to thereactor.

In operation 606, processing of a substrate is initiated in the reactorusing the settings of the tuning knobs, as identified in the model. Asshown in FIG. 3, model 230 j is associated with a desired processingstate 234 and tuning knob settings 232. Thus, the tuning knob settingsused in operation 606 will be obtained from a model initially, e.g.,such as when a reactor is first used after a wet clean operation, andneeds to be seasoned. In operation 607, during the processing of thesubstrate, data streams from the sensors are communicated to amultivariate processor while processing the substrate in the reactor. Asmentioned above, the multivariate processor will include a machinelearning engine, which is utilized to identify and learn the types ofmodifications required to the tuning knobs in order to place theprocessing back in accordance with the desired processing state values.In operation 608, the current processing state values are monitored byexamination of the data streams by the multivariate processor.

In operation 610, the current processing state values are compared tothe desired processing state values by the multivariate processor. Ifthe current processing state is now in accordance with the desiredprocessing state, the processing state is verified in operation 611. Byway of example, at this point it can be said that the seasoningoperation is complete, since the current processing state matches thedesired processing state. In another embodiment, before the currentprocessing state matches the desired processing state, the multivariateprocessor can identify a compensation vector that identifies or is usedto identify adjustments that can be made to the tuning knobs in order tobring the reactor into a state that matches the desired processingstate.

This operation can be performed to expedite placing a reactor that isundergoing seasoning quickly into a production state. As mentionedabove, this is advantageous because it is no longer required thatreactors be seasoned for arbitrarily long periods of time, as it is nowpossible to identify when the reactor has actually reached the desiredprocessing state or is adjusted via the tuning knobs to reach theprocessing state quicker.

If the processing is being performed on a substrate during productionwafer processes, the operation can continue to 612, were a compensationvector is generated to identify adjustments to be made to the currentprocessing state in order to shift processing toward the desiredprocessing state. In operation 614, the compensation vector istransformed into real space adjustments that identify each knob to beadjusted and an amount of said adjustment. In operation 616, theadjustment is applied to each specified knob of the reactor.

If processing is to continue in operation 618, the monitoring andapplying of adjustments 620 can continue in order to prevent drift tooccur as additional processing of wafers continue in the reactor. Asmentioned above, it is anticipated that process drift will occur as thereactor continues to be used to process wafers, as the reactor wallswill build up material and consumable parts may be used up over time.However, by making the adjustments using the multivariate processor thatuses machine learning, it is possible to make adjustments to the tuningknobs in order to maintain the current processing state withinsubstantial balance of the desired processing state.

FIG. 10 illustrates an example of an operation of bringing a reactor upafter reactor seasoning operation 704 a, in accordance with oneembodiment. In operation 702, the reactor is cleaned and/or maintenanceis performed on the reactor. During this operation, the reactor may beopened, parts replaced, parts cleaned, parts reconditioned, and/orsimply assembled for operation. As mentioned, reactor seasoning 704 aincludes reactor verification of steady-state for production processingof substrates for a recipe in operation 706. This processing will beperformed to monitor the processing state of the reactor during thereactor seasoning operations. As mentioned above, the seasoningoperations may include running the reactor using a plurality ofsubstrates that are designed to season the chamber, and the substratesprocessed will be chosen to resemble or mimic the type of processing tobe performed by the production substrates using the desired or targetedprocess.

In one embodiment, the reactor verification of steady-state is achievedwhen the multivariate processing determines that the current processingstate has matched or substantially matches the desired processing state,as per the processing performed by the machine learning engine 180, asdescribed with reference to FIG. 2. Once the chamber is determined tohave been seasoned, and is ready for production processing, the methodmoves to operation 708 where production processing of substrates canbegin for a recipe in the reactor.

During the processing of one or more wafers in the reactor, driftcontrol can be processed during production processing in operation 710.As mentioned above, drift control includes utilizing the multivariateprocessor in order to identify when a current processing state isdrifting away from the desired processing state, which may occur as moresubstrates are processed. In one embodiment, by continually updating theadjustment to the tuning knobs, it is possible to maintain the reactorcurrent processing state in line with the desired processing state, toprolong the effective and useful operation of the reactor for morewafers.

FIG. 11 illustrates another embodiment, where the reactor may be cleanedor maintained in operation 702. In this example, reactor seasoning 704 bincludes additional operations to the process of reaching a steady-stateby the reactor during seasoning. In this method, reactor verification ofsteady-state for production processing of substrates for recipe is begunin operation 706, as was done in FIG. 10. During the verificationprocess, which includes running one or more seasoning wafers through thereactor, the method includes operation 712, where a determination ismade whether the current processing state is within bound to enabledrift control. Processing state is considered to be within bound when itis determined or predetermined that adjustments to the one or moretuning knobs can be performed, and that desired processing state can bereached.

In this example, drift control is used in the context of adjustingsettings of the tuning knobs of the reactor during reactor seasoning, inorder to place the reactor in a ready state for production processingwithout continuing to process seasoning wafers. In operation 714, if itis determined that the processing state is within bound, thencompensation is applied to the tuning knobs of the reactor in order toexit the reactor seasoning early. By way of example, some seasoningoperations may take several hours, in a neighborhood of 8 to 12 hours,and conventional techniques have erred on the side of caution andprocessed seasoning wafers for the longer period of seasoning, based onexperience. However, operators that do this type of seasoning byexperience, by virtue of their erring on the side of caution, willseason a reactor for longer than it's needed.

In accordance with one embodiment, the processing 704 b will enableidentification of when the reactor can be adjusted by changing tuningknobs to bring the reactor state consistent with the desired processingstate values, without continuing to process seasoning wafers. Thus, thereactor can be placed into actual production for processing productionwafers sooner, and avoid the cost of seasoning and avoid the wasted costof being unable to use the reactor for production processing to thefullest extent possible. In this embodiment, operation 708 and 710 areprocessed similar to that of FIG. 10.

Through the embodiments described herein, processing tools are beingmade intelligent enough to take decisions on how to maintain correctprocessing state of the reactor with minimum user input. Expanding thisconcept is very powerful in the semiconductor processing field, as thisreduces the dependency of such tool's ability to maintain its conditionfrom existing fabrication control systems and or human operatorexperience. As noted human operator experience is also not a reliablemethod, as each operator/engineer will have his or her own methodologyand once they move on, new personnel needs to be trained. The describedembodiments are especially different conventional techniques, as themachine learning engine 180 of the multivariate processing uses actualreal-time sensor data of tool to define its state. This is in contrastto many current techniques, which rely solely on monitor wafers andassociated metrology tools. As noted above, embodiments of the presentinvention may use monitor wafers (and etch rate data), but its use isnow for the purpose of verifying the learned compensation valuesidentified by the multivariate processing in order to shift the processback to a real time desired processing state.

As mentioned, a unique feature of the disclosed embodiments is that theuser's experience is not directly needed to perform chamber matching.These reactors are very complex and will usually have users of variousexperience levels. And, many users have limited understanding of reactoroperations and may spend enumerable hours tuning to achieve a somewhataccurate processing state for the reactor. Using the described machinelearning engine will ensure that tools are equipped with an advancelevel virtual user and systems can take correct decisions in a timelyand real-time manner. This aspect is not only useful to toolmanufactures that characterize processes for customers, but also fortool customers that need to provision their own processes on toolspurchased from tool suppliers.

Through an extension of the various teachings described herein, themachine learning engine may also be applied for Rapid ProcessDevelopment (RPD), which includes developing initial process trends andfeeding that to the machine learning engine. The engine then tries topredict the desired state and can tune the process much faster. Thiswill reduce process development time and will reduce dependence on userexperience level.

In one embodiment, the controller 120, described with reference to FIG.1 above may include a processor, memory, software logic, hardware logicand input and output subsystems from communicating with, monitoring andcontrolling a plasma processing system. The controller 120 may alsohandle processing of one or more recipes including multiple set pointsfor various operating parameters (e.g., voltage, current, frequency,pressure, flow rate, power, temperature, etc.), e.g., for operating aplasma processing system. Furthermore, although more detailed exampleswere provide with reference to etching operations (e.g., etching tools),it should be understood that the operations can equally be utilized fordeposition operations (e.g., deposition tools). For example, in theverification operations, instead of verifying etch performance, theverification can be of deposition performance. Deposition performancecan be quantified in various ways, and without limitation, various typesof metrology methods and/or tools may be used. Furthermore, depositionperformance may be measured, sensed, approximated, and/or tested in-situor off-line.

In some implementations, a controller is part of a system, which may bepart of the above-described examples. Such systems can comprisesemiconductor processing equipment, including a processing tool ortools, chamber or chambers, a platform or platforms for processing,and/or specific processing components (a wafer pedestal, a gas flowsystem, etc.). These systems may be integrated with electronics forcontrolling their operation before, during, and after processing of asemiconductor wafer or substrate. The electronics may be referred to asthe “controller,” which may control various components or subparts ofthe system or systems. The controller, depending on the processingrequirements and/or the type of system, may be programmed to control anyof the processes disclosed herein, including the delivery of processinggases, temperature settings (e.g., heating and/or cooling), pressuresettings, vacuum settings, power settings, radio frequency (RF)generator settings, RF matching circuit settings, frequency settings,flow rate settings, fluid delivery settings, positional and operationsettings, wafer transfers into and out of a tool and other transfertools and/or load locks connected to or interfaced with a specificsystem.

Broadly speaking, the controller may be defined as electronics havingvarious integrated circuits, logic, memory, and/or software that receiveinstructions, issue instructions, control operation, enable cleaningoperations, enable endpoint measurements, and the like. The integratedcircuits may include chips in the form of firmware that store programinstructions, digital signal processors (DSPs), chips defined asapplication specific integrated circuits (ASICs), and/or one or moremicroprocessors, or microcontrollers that execute program instructions(e.g., software). Program instructions may be instructions communicatedto the controller in the form of various individual settings (or programfiles), defining operational parameters for carrying out a particularprocess on or for a semiconductor wafer or to a system. The operationalparameters may, in some embodiments, be part of a recipe defined by aprocess that is engineered to accomplish one or more processing stepsduring the fabrication of one or more layers, materials, metals, oxides,silicon, silicon dioxide, surfaces, circuits, and/or dies of a wafer.

The controller, in some implementations, may be a part of or coupled toa computer that is integrated with, coupled to the system, otherwisenetworked to the system, or a combination thereof. For example, thecontroller may be in the “cloud” or all or a part of a fab host computersystem, which can allow for remote access of the wafer processing. Thecomputer may enable remote access to the system to monitor currentprogress of fabrication operations, examine a history of pastfabrication operations, examine trends or performance metrics from aplurality of fabrication operations, to change parameters of currentprocessing, to set processing steps to follow a current processing, orto start a new process. In some examples, a remote computer (e.g. aserver) can provide process recipes to a system over a network, whichmay include a local network or the Internet. The remote computer mayinclude a user interface that enables entry or programming of parametersand/or settings, which are then communicated to the system from theremote computer. In some examples, the controller receives instructionsin the form of data, which specify parameters for each of the processingsteps to be performed during one or more operations. It should beunderstood that the parameters may be specific to the type of process tobe performed and the type of tool that the controller is configured tointerface with or control. Thus as described above, the controller maybe distributed, such as by comprising one or more discrete controllersthat are networked together and working towards a common purpose, suchas the processes and controls described herein. An example of adistributed controller for such purposes would be one or more integratedcircuits on a chamber in communication with one or more integratedcircuits located remotely (such as at the platform level or as part of aremote computer) that combine to control a process on the chamber.

Without limitation, example systems may include a plasma etch chamber ormodule, a deposition chamber or module, a spin-rinse chamber or module,a metal plating chamber or module, a clean chamber or module, a beveledge etch chamber or module, a physical vapor deposition (PVD) chamberor module, a chemical vapor deposition (CVD) chamber or module, anatomic layer deposition (ALD) chamber or module, an atomic layer etch(ALE) chamber or module, an ion implantation chamber or module, a trackchamber or module, and any other semiconductor processing systems thatmay be associated or used in the fabrication and/or manufacturing ofsemiconductor wafers.

As noted above, depending on the process step or steps to be performedby the tool, the controller might communicate with one or more of othertool circuits or modules, other tool components, cluster tools, othertool interfaces, adjacent tools, neighboring tools, tools locatedthroughout a factory, a main computer, another controller, or tools usedin material transport that bring containers of wafers to and from toollocations and/or load ports in a semiconductor manufacturing factory.

FIG. 12 is a simplified schematic diagram of a computer system forimplementing embodiments. It should be appreciated that the methodsdescribed herein may be performed with a digital processing system, suchas a conventional, general-purpose computer system. Special purposecomputers, which are designed or programmed to perform only one functionmay be used in the alternative. The computer system includes a centralprocessing unit (CPU) 804, which is coupled through bus 810 to randomaccess memory (RAM) 828, read-only memory (ROM) 812, and mass storagedevice 814. System controller program 808 resides in random accessmemory (RAM) 828, but can also reside in mass storage 814.

Mass storage device 814 represents a persistent data storage device suchas a floppy disc drive or a fixed disc drive, which may be local orremote. Network interface 830 provides connections via network 832,allowing communications with other devices. It should be appreciatedthat CPU 804 may be embodied in a general-purpose processor, a specialpurpose processor, or a specially programmed logic device. Input/Output(I/O) interface provides communication with different peripherals and isconnected with CPU 804, RAM 828, ROM 812, and mass storage device 814,through bus 810. Sample peripherals include display 818, keyboard 822,cursor control 824, removable media device 834, etc.

Display 818 is configured to display the user interfaces describedherein. Keyboard 822, cursor control 824, removable media device 834,and other peripherals are coupled to I/O interface 820 in order tocommunicate information in command selections to CPU 804. It should beappreciated that data to and from external devices may be communicatedthrough I/O interface 820. The embodiments can also be practiced indistributed computing environments where tasks are performed by remoteprocessing devices that are linked through a wire-based or wirelessnetwork.

Embodiments may be practiced with various computer system configurationsincluding hand-held devices, microprocessor systems,microprocessor-based or programmable consumer electronics,minicomputers, mainframe computers and the like. The embodiments canalso be practiced in distributed computing environments where tasks areperformed by remote processing devices that are linked through anetwork.

With the above embodiments in mind, it should be understood that theembodiments can employ various computer-implemented operations involvingdata stored in computer systems. These operations are those requiringphysical manipulation of physical quantities. Any of the operationsdescribed herein that form part of the embodiments are useful machineoperations. The embodiments also relate to a device or an apparatus forperforming these operations. The apparatus may be specially constructedfor the required purpose, such as a special purpose computer. Whendefined as a special purpose computer, the computer can also performother processing, program execution or routines that are not part of thespecial purpose, while still being capable of operating for the specialpurpose. Alternatively, the operations may be processed by a generalpurpose computer selectively activated or configured by one or morecomputer programs stored in the computer memory, cache, or obtained overa network. When data is obtained over a network the data may beprocessed by other computers on the network, e.g., a cloud of computingresources.

One or more embodiments can also be fabricated as computer readable codeon a computer readable medium. The computer readable medium is any datastorage device that can store data, which can thereafter be read by acomputer system. Examples of the computer readable medium include harddrives, network attached storage (NAS), read-only memory, random-accessmemory, CD-ROMs, CD-Rs, CD-RWs, magnetic tapes and other optical andnon-optical data storage devices. The computer readable medium caninclude computer readable tangible medium distributed over anetwork-coupled computer system so that the computer readable code isstored and executed in a distributed fashion.

Although the method operations were described in a specific order, itshould be understood that other housekeeping operations may be performedin between operations, or operations may be adjusted so that they occurat slightly different times, or may be distributed in a system whichallows the occurrence of the processing operations at various intervalsassociated with the processing, as long as the processing of the overlayoperations are performed in the desired way.

For more information regarding methods for inspecting process chambersand/or consumable parts, reference may be made to U.S. patentapplication Ser. No. 14/961,756, filed on Dec. 7, 2015 and entitled“Estimation of Lifetime Remaining for Consumable Part in a SemiconductorManufacturing Chamber,” which is incorporated by reference herein.

For more information on methods for monitoring process conditions andmethods for adjusting settings, reference may be made to U.S.Provisional Patent Application No. 62/370,658, filed on Aug. 3, 2016,entitled “Methods and Systems for Monitoring Plasma Processing Systemsand Advanced Process and Tool Control,” U.S. Pat. No. 6,622,286,entitled “Integrated electronic hardware for wafer processing controland diagnostic,” U.S. Pat. No. 8,295,966, entitled “Methods andapparatus to predict etch rate uniformity for qualification of a plasmachamber,” U.S. Pat. No. 8,983,631, entitled “Arrangement for identifyinguncontrolled events at the process module level and methods thereof,”U.S. Pat. No. 8,473,089, entitled “Methods and apparatus for predictivepreventive maintenance of processing chambers,” U.S. Pat. No. 8,271,121,entitled “Methods and arrangements for in-situ process monitoring andcontrol for plasma processing tools,” and U.S. Pat. No. 8,538,572,entitled “Methods for constructing an optimal endpoint algorithm,” allof which are assigned to Lam Research Corporation, the assignee of thepresent application and each of which are incorporated herein for allpurposes.

For additional information regarding machine learning algorithms,phenomenological models and associated processes, reference may be madeto a Theses entitled “Virtual Metrology for Semiconductor ManufacturingApplications,” by Bertorelle Nicola, University of Padua, Department ofInformation Engineering, dated 28 Jun. 2010; a Theses entitled“Statistical Methods for Semiconductor Manufacturing,” by Gian AntonioSusto, Universita Degli Studi di Padova, School in InformationEngineering, January 2013; and a paper entitled “Etching characteristicsand mechanisms of the MgO thin films in the CF4/Ar inductively coupledplasma,” by A. Efremov, et al. Department of Electronic Devices andMaterials Technology, Sate University of Chemistry and Technology, 7, F.Engels St., 15300 Ivanovo, Russia, Jan. 12, 2007, each of which isherein incorporated by reference.

Further, embodiments and any specific features described in the aboveincorporated by reference documents and applications may be combinedwith one or more features described herein, to define or enable specificembodiments.

Although the foregoing embodiments have been described in some detailfor purposes of clarity of understanding, it will be apparent thatcertain changes and modifications can be practiced within the scope ofthe appended claims. Accordingly, the present embodiments are to beconsidered as illustrative and not restrictive, and the embodiments arenot to be limited to the details given herein, but may be modifiedwithin the scope and equivalents of the appended claims.

1. A system for controlling processing state of a plasma process,comprising: a plasma reactor having a plurality of tuning knobs formaking settings to operational conditions of the plasma reactor; aplurality of sensors of the plasma reactor, each of the plurality ofsensors is configured to produce a data stream of information duringoperation of the plasma reactor for carrying out the plasma process; anda controller of the plasma reactor is configured to execute amultivariate processing that is configured to use as input desiredprocessing state values that define intended measurable conditionswithin a processing environment of the plasma reactor and identifycurrent plasma processing values, the multivariate processing using amachine learning engine that receives as inputs the desired processingstate values and data streams from the plurality of sensors duringprocessing of the plasma process, and the machine learning engine isconfigured to identify current processing state values used to produce acompensation vector, such that the compensation vector definesdifferences between the desired processing state values and the currentprocessing state values; wherein the controller is further configured toexecute a compensation processing operation that transforms thecompensation vector expressed in terms of measured conditions within theprocessing environment to changes of specific one or more of the tuningknobs of the plasma reactor.
 2. The system of claim 1, wherein thecontroller is further configured to generate changes for the tuningknobs of the plasma reactor to cause a change in the measurableconditions of the processing environment of the reactor.
 3. The systemof claim 1, wherein the machine learning engine is configured toperiodically receive measured substrate performance data regarding oneor both of etch rate measurements or monitor wafer measurements, themeasured substrate performance data is used to make adjustments to thedesired processing state values, which in turn cause adjustments to thecompensation vector and the changes to said one or more of the tuningknobs.
 4. The system of claim 3, wherein the machine learning engine isconfigured to perform verification of the current processing statevalues with real data obtained from one or both of etch ratemeasurements or monitor wafer measurements.
 5. The system of claim 1,wherein the machine learning engine further receives sensitivity dataregarding sensor signals to compensation of tuning knobs.
 6. The systemof claim 1, wherein the machine learning engine further receives reactorwall surface dynamics for use by a phenomenological model that definesplasma dynamics within the processing environment in terms of said datastreams produced by said plurality of sensors of the plasma reactor. 7.The system of claim 1, wherein the system is configured to be executedin one or more operational phases, wherein one operational phaseincludes, during plasma reactor seasoning phase that uses non-productionsubstrates, the plasma reactor seasoning phase being monitored by thecontroller by executing the multivariate processing to identify when thecurrent processing state values are within a bound that enablesadjustment of the tuning knobs to place the plasma reactor in a statethat is ready for processing production substrates and enablesdiscontinuing of the plasma reactor seasoning phase.
 8. The system ofclaim 1, wherein the system is configured to be executed in one or moreoperational phases, wherein one operational phase includes, during aproduction phase that uses production substrates, the controllerexecutes the multivariate processing to identify when the currentprocessing state values are within a bound that enables adjustment ofthe tuning knobs to compensate for drift in the plasma process.
 9. Thesystem of claim 8, wherein the compensation for drift occurs multipletimes during said production phase, and the adjustments in the tuningknobs are calculated to move the processing environment closer to thedesired processing state values as measured by the plurality of sensors.10. A system for controlling processing state of a plasma process,comprising: a plasma reactor having a plurality of tuning knobs formaking settings to operational conditions of the plasma reactor; aplurality of sensors of the plasma reactor, each of the plurality ofsensors is configured to produce a data stream of information duringoperation of the plasma reactor for carrying out the plasma process; anda controller of the plasma reactor is configured to execute amultivariate processing that is configured to use as input desiredprocessing state values that define intended measurable conditionswithin a processing environment of the plasma reactor and identifycurrent plasma processing values, the multivariate processing uses amachine learning engine that takes as inputs, the desired processingstate values, data streams from the plurality of sensors duringprocessing of the plasma process, and sensitivity data regarding sensorsignals to compensation of tuning knobs, and the machine learning engineis configured to identify current processing state values used toproduce a compensation vector, wherein the compensation vectoridentifies differences between the desired process state values and thecurrent processing state values.
 11. The system of claim 10, wherein thecontroller is further configured to execute a compensation processingoperation that transforms the compensation vector expressed in terms ofmeasured conditions within the processing environment to changes ofspecific one or more of the tuning knobs of the plasma reactor.
 12. Thesystem of claim 11, wherein the controller is further configured toinstruct changes to one or more of the tuning knobs of the plasmareactor to cause a change in the measurable conditions of the processingenvironment of the reactor.
 13. The system of claim 10, wherein themachine learning engine is configured to periodically receive measuredsubstrate performance data regarding one or both of etch ratemeasurements or monitor wafer measurements.
 14. They system of claim 13,wherein a metrology tool is used to measure substrate performance datafrom one or more substrates processed by the plasma reactor.
 15. Thesystem of claim 10, wherein the system is configured to be executed inone or more operational phases, wherein one operational phase includes,during plasma reactor seasoning phase that uses non-productionsubstrates, the plasma reactor seasoning phase being monitored by thecontroller by executing the multivariate processing to identify when thecurrent processing state values are within a bound that enablesadjustment of the tuning knobs to place the plasma reactor in a statethat is ready for processing production substrates and enablesdiscontinuing of the plasma reactor seasoning phase.
 16. The system ofclaim 10, wherein the system is configured to be executed in one or moreoperational phases, wherein one operational phase includes, during aproduction phase that uses production substrates, the controllerexecutes the multivariate processing to identify when the currentprocessing state values are within a bound that enables adjustment ofthe tuning knobs to compensate for drift in the plasma process.